SQL vs. NoSQL: Which Database Should You Use?

SQL vs. NoSQL: Which Database Should You Use?

Managing data is an indispensable task in modern technology. At present, there are two dominant approaches to managing data:

  • SQL databases
  • NoSQL databases

SQL databases store data in related tables of rows and columns. On the other hand, NoSQL databases store unstructured or semi-structured data such as documents and images.

How does this difference in data structure impact their use and application? And what advantages do each hold over the other?

These are the questions we'll be tackling in this blog post. So keep reading if you want to learn more.

What is SQL?

Structured Query Language (SQL) is a programming language that allows us to add, retrieve, and modify data from database tables. These actions are known as "queries" in programming.

So, SQL databases are those that use SQL to run their queries. These databases are used to store structured data in the form of related tables.

SQL tables consist of rows and columns, where each row is a single record, and each column is a single piece of information about that record.

An SQL database consists of multiple tables related to each other through shared columns. Thus, SQL databases are classified as relational databases.

Key Features of SQL Databases

Having covered the basics, let's dive into the technical features of SQL databases. The most salient of these features are:

Structured Data

They store highly structured data in related tables of rows and columns. Thus, you need to carefully plan the structure of the tables and their relationships before you deploy your database.

High Security

Several in-built constraints give SQL databases higher security than other database types. This is achieved through restrictions on data types, primary keys, secondary keys, etc.

Vertical Scalability

You can increase an SQL database's performance by upgrading the database server's hardware. However, adding more servers to the network is not very cost-effective.

ACID Compliant

SQL databases follow the ACID (Atomicity, Consistency, Isolation, Durability) principle, whose basic tenets are

  • Atomicity: All database transactions must either result in complete success or complete failure. They cannot be left partial or incomplete, even in the case of system failure.
  • Consistency: Corruption and unauthorized modification of data must be prevented through measures for authentication and validation.
  • Isolation: You can run concurrent queries and transactions, but they cannot affect each other.
  • Durability: A complete and finalized database transaction cannot be rolled back, even in the case of system failure.

Some popular SQL database management systems (DBMS) are:

  • MySQL
  • Oracle
  • Microsoft SQL Server
  • PostgreSQL
  • SQLite
  • IBM DB2

What is NoSQL?

Unlike SQL databases, NoSQL (Not only SQL) stores unstructured or semi-structured data, usually in the form of documents such as JSON, XML, graphs, images, etc.

Since NoSQL databases don't adhere to a strict schema, they are more flexible and don't need extensive planning before deployment. As a result, we have greater freedom to utilize the data structure that is best suited for our chosen application, along with greater ease in adding and modifying data fields and attributes.

Another advantage of NoSQL databases is that they can handle large datasets, which is not valid for SQL.

On the other hand, NoSQL is a relatively new database format that is not yet as well-developed and mature as SQL. This means that there are more educational and supporting materials for SQL databases.

Key Features of NoSQL Databases

Now, let's explore some features of NoSQL databases that are important from a technical standpoint:

Unstructured Data

Data is not stored in related tables of rows and columns. Instead, they're stored in any of the following formats:

  • Encoded Documents in formats such as XML, JSON, etc.
  • Key-Value Databases where data is stored in key-value pairs.
  • Graph Databases store graphical data showing the relationships between different datasets.
  • Column Data where data is stored in cells that consist of multiple columns but no rows.

Horizontal Scalability

Unlike SQL databases, you can easily increase the performance of a NoSQL database by adding more servers and devices to the network.

High Performance and Flexibility

Using unstructured data and the ease of horizontal scaling ensures that NoSQL databases can handle huge volumes of data in many different formats.

Note: Technically, SQL is a query language for relational databases, while NoSQL is an umbrella term for databases that don't use tables of rows and columns. Therefore, each NoSQL DBMS uses its own query language.

The following are some of the most popular NoSQL DBMS in the market today:

  • MongoDB
  • Cassandra
  • Couchbase
  • Amazon DynamoDB
  • Google Cloud Bigtable
  • Apache HBase
  • Redis
  • Neo4j

When Should You Use SQL?

Despite their relative inflexibility and restrictions on data volume, SQL databases are still a good choice in many situations. Some of them are:

  • Structured Data: SQL databases are ideal for handling data that can be arranged into tables of rows and columns.
  • Small to Medium-sized Data: SQL databases can provide a simple, reliable solution for storing and retrieving datasets that aren't too large.
  • Data Validity and Security: SQL databases have many constraints that facilitate the validity of data. If security is a high priority, then SQL is probably the better option.
  • Transactional Applications: SQL databases are great for transactional applications requiring ACID properties, i.e., all transactions must be completed or rolled back.
  • Business Applications: SQL databases are commonly used to store crucial data on people and products. Examples of such applications include Enterprise Resource Planning (ERP) systems, accounting systems, inventory management systems, and e-commerce websites.
  • Ad-hoc Queries: These are database queries that cannot be solved with standard, predefined queries. SQL is well-suited for such problems since it allows us to write complex queries that can retrieve and manipulate large amounts of data.

SQL Use Cases

Now that you have a general idea of when you should use SQL, here are some specific areas and fields that require its use:

  • Banking and Financial Applications
  • Healthcare Applications
  • Human Resources Applications
  • E-commerce Applications
  • Content Management Systems (CMS)

When Should You Use NoSQL?

As you already know, we use NoSQL in cases where the use of an SQL database is not justified. Below is a list of some common cases that require NoSQL:

  • High Scalability: Thanks to its support for horizontal scaling, NoSQL databases are great for dealing with large and ever-increasing datasets.
  • Flexible Data Models: If your application needs to deal with a wide variety of data in different formats, then NoSQL databases are ideal for the job.
  • Big Data: NoSQL databases are well-suited for Big Data applications, as they can handle large amounts of unstructured or semi-structured data.
  • Rapid Application Development: NoSQL databases can be implemented quickly at some cost to data validity. They can also be easily adapted to changing requirements.
  • High Availability and Fault Tolerance: Data in many NoSQL databases are replicated across many different servers, making them highly available and fault-tolerant.

Specific Use Cases

There are a whole bunch of ubiquitous applications that require NoSQL. Some of them are:

  • E-commerce Applications
  • Social Media Platforms
  • IoT (Internet of Things) Applications
  • Real-time Analytics
  • Gaming Applications

Note: Since many organizations deal with structured and unstructured data, they often combine SQL and NoSQL databases to handle their differing requirements.

Summary: SQL vs. NoSQL

By now, we have discussed many differences between SQL and NoSQL databases. These differences are summarized in the table below:

SQL Databases

NoSQL Databases

SQL stands for Structured Query Language.

NoSQL stands for Not only SQL.

They are relational databases that consist of data stored in related tables of rows and columns.

They are non-relational databases that consist of data stored in key-value pairs, graphs, or documents.

They store highly structured data.

They store unstructured or semi-structured data.

They are ideal for structured data of small or medium size.

They are ideal for vast datasets.

Performance can be boosted by upgrading the server hardware (vertical scaling).

Performance can be boosted by adding new servers (horizontal scaling).

Prioritizes security and organization at the expense of flexibility and scalability.

Prioritizes scalability, flexibility, and deployment speed at some expense to security.

Primarily used for business and transactional applications.

Primarily used for big data and social media applications.

Examples include MySQL, Oracle, PostgreSQL, MS SQL Server, etc.

Examples include MongoDB, Cassandra, Amazon DynamoDB, Bigtable, etc.


SQL and NoSQL databases are very different but popular approaches to managing data. You can use one or the other depending on the needs of your application.

If your dataset is not too large and can be structured in tables of rows and columns, then SQL databases are ideal for you.

On the other hand, if you need to deal with enormous and unstructured datasets, then NoSQL databases are a great fit.

The main point is that whatever you choose, make sure that they fit the requirements of your application.